S3 Analysis of Longitudinal Study of Australian Children
Introduction
Growing up Australia : Longitudinal Study of Australian Children (LSAC) aims to examine the impact of the Australian social, economic, and cultural environment over the life course to identify the opportunities for early intervention. LSAC is a collaboration between the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice from leading researchers in the form of a Consortium Advisory Group.
Recruitment for LSAC was undertaken between March and November 2004, with over 10,000 families agreeing to participate. Since 2004, data has been collected every two years, with mail-out and online questionnaires set out between each main collection. The study recruited from two cohorts, the birth cohort (B) where children were born between March 2003 and February 2004, and the kindergarten cohort (K) where children were born between March 1999 and February 2000, i.e. in kindergarten at the time of recruitment.
Table 1 shows the sample size over collection waves and years. There was a reduced set of data collected in wave 9.1 due to COVID-19 restrictions. This data did not have measured heights and weights at is removed from the analysis for DiSCAO.
| cohort | Wave 1 2004 | Wave 2 2006 | Wave 3 2008 | Wave 4 2010 | Wave 5 2012 | Wave 6 2014 | Wave 7 2016 | Wave 8 2018 | Wave 9.1 2020 |
|---|---|---|---|---|---|---|---|---|---|
| B | 5107 | 4603 | 4386 | 4231 | 4085 | 3758 | 3375 | 3117 | 2017 |
| K | 4980 | 4459 | 4331 | 4161 | 3956 | 3529 | 3076 | 3023 | 1789 |
Figure 1, shows how the LSAC sample was collected using the dual cohort design, this allows multiple measurements of age groups separated by 4 years or 2 collection waves.
Figure 1: dual cohort cross sectional design of LSAC, Australian Institute of Family Studies (2018)
Australian Institute of Family Studies. (2018). Longitudinal Study of Australian Children Data User Guide – December 2018. Melbourne: Australian Institute of Family Studies.
The health check “CheckPoint” data collection was a one-off physical health and biomarker module added for the birth cohort (B) between the LSAC waves 6 and 7, collected between February 2015 and March 2016. The B cohort study child and one of their parents were invited to participate in a clinical appointment or short home visit. The B cohort study children were between the ages of 11-12 at the time of measurement.
Body composition data was collected in waves 4 - 8, however, due to larger variances in fat mass and a large number of responses being 100% or 0% fat mass, this data was used for validation of analysis using the CheckPoint data.
Methods
Descriptive summaries
Data was visually summarised with plots by age-gender-BMI groups. Medians and confidence, weighted to adjust for sampling strategies, summarised are used as inputs for the DiSCAO and presented in the Appendix.
Estimating % Fat mass
Body composition is a required input into the energy regulation equations presented by Hall et al 2013 and used in Chapter 4 Section 4.2.2.3.
\[ \Delta FFM_{i,j,k}=\frac{pEnS_{i.j.k}+G_{j,k}}{\hat{\rho_{FFM, i,j,k}}}\] and
\[ \Delta FM_{i,j,k}=\frac{(1-p_{i,j,k})EnS_{i.j.k}-G_{j,k}}{\rho_{FM}}\] Such that \(\hat{\rho_{FFM, i,j,k}}\) and \(p_{i,j,k}\) rely on estimates of body composition.
\[ \hat{\rho_{FFM, i,j,k}} = (837+4.3 \times FFM_{i,j,k}) \times 4.18\]
With the p-ratio defined by Forbes 1987. \[ p_{i,j,k} = \frac{C_{i,j,k}}{C_{i,j,k}+FM_{i.j.k}}\]
and where,
\[C_{i,j,k}=10.4 \times\frac{\hat{\rho_{FFM, i,j,k}}}{\rho_{FM}}\]
\(i=\{ \text{BMI categoried} \}\)
\(j=\{ \text{Age groups} \}\)
\(k=\{ \text{Gender} \}\)
these equations were constructed using linear regression assuming normal distribution errors on a relatively small sample (n=416). These equations will also need to be applied to the main LSAC waves to give FFM% estimates for the required subgroups Linear regression was not appropriate for extrapolation.
Beta regression was used to develop equations to predict the %FFM for the “Checkpoint” data collection. These equations are then applied to the general LSAC data to estimate the FFM% for all sub-groups needed for the model.
A step-wise algorithm was used to sequentially test model terms ranging including combinations of gender, height, weight, and 2-way and 3-way interactions. Additionally, polynomial terms for height and weight were considered up to the 3rd order (height^3), this gives the model freedom to choose non-linear relationships.
The predicted %FFM was estimated using the Coretes-Castell equations and the beta regression to assess the appropriateness of each method.
Results
Descriptive summaries
Individual level BMI
These histograms of BMI over time (Figure 2) and gender-age-groups show the age sampling of the dual cohort of LSAC. The shift in shape is a key underlying dynamics that reflects changes in the prevalence of obesity in Australian children.
Figure 2: Comparison of BMI distribution over time and by gender and age groups
Body weight
Figure 3 shows the reported body weight over age, by gender and BMI categories. In general body weight increases with age, overweight and obese body weights increase at a faster rate and males have a higher body weight than females. It should be noted that the data is presented as cross-sectional when there are repeat measures.
Figure 3: Combined wave, Body weight by age, gender and BMI category
Figure 4 plots reported body weight from the national health survey (NHS) for all ages.
Figure 4: 2007 National Health Survey: Body weight by age, gender and BMI category
Height
Figure 5 plots the age-gender-BMI hieghts in the LSAC data.Figure 5: Combined wave, Height by age, gender and BMI category
Figure 6: 2007 National Health Survey: Height by age, gender and BMI category
Body composition
LSAC CheckPoint data
Figures 7 and 8 are 3D plots of the LSAC CheckPoint data for males and females. These plots can be examined by spinning the plot areas to look at the relationships between body weight, height and %fat-mass.
Figure 7: 3D plot of Males Height, weight and % fat-mass for LSAC CheckPoint data
Figure 8: 3D plot of Females Height, weight and % fat-mass for LSAC CheckPoint data
Beta-Regression model
The beta-regression coefficients were fitted for the health CheckPoint data using weight and height. The resulting equations are used to predict the % fat mass for each gender and are applied to the whole LSAC data. The following sections examine model performance.
Males
\[ FM_{Males} = \text{Exp}(-5.236806 - \text{weight}^{2}\times 1.057483e^{-3} - \text{height}^2 \times 2.921958e^{-4} \\ + \text{weight} \times 1.319295e^{-1} + \text{height} \times 4.529665e^{-2} + \text{weight}^2 \times \text{height}^2 \times 1.690558e^{-8} - \text{weight} \times \text{height} \times 1.492729e^{-4})\]
Females
\[ FM_{females} = \text{Exp}(-5.236806 - \text{weight}^{2}\times 1.057483e^{-3} - \text{height}^2 \times 2.921958e^{-4} + \text{weight} \times 1.319295e^{-1} \\ + \text{height} \times 4.529665e^{-2} + \text{weight}^2 \times \text{height}^2 \times 1.690558e^{-8} - \text{weight} \times \text{height} \times 1.492729e^{-4} \\ + 7.542881e^{-2} - \text{weight}^{2}\times 2.553074e^{-4} + \text{height}^2 \times 7.785382e^{-6} + \text{weight}^2 \times \text{height}^2 \times 7.933846e^{-9} )\]
Residuals
Beta regression The fitted vs residual plot (Figure 8) shows a generally good fit with a slight under estimate in higher %fat percentage and overestimates in low %fat percentage. A good fit would be seen when the red loess line follows the dashed linear (x=y) line.
Figure 8: Beta regression residuals against fitted obseravtions
The distribution of the beta regression residuals are shown in Figure 9.
Figure 9: Distribution beta regression residuals
Bland-Altman Plot
The Bland-Altman plot, shows the difference between observed and predicted against observed fat percentage in the CheckPoint data.
Figure 10: Bland-Altman plots residuls vs observed
Extrapolation
The beta regression model was used to predict the remaining LSAC data. Figure 11 shows the age-gender-BMI curves for predicted % fat mass.
Figure 11: Extrapolation of Beta regression model onto LSAC data
Beta Predictions againts observed LSAC wave 4-8 data
As mentioned in the methods, the main wave of the LSAC data has %fat mass collected between waves 4-8. Figure 12 shows the fit of the Beta regression predictions against the wave 4-8 data collected. The plot has coloured points by “Data quality”, observed fat-mass from waves 4-8 with fat-mass = 0 or greater than 80% was considered to be possibly an error.
The plot shows that the predicted estimates follow the linear line with deviations in the upper and lower ends.
Figure 12: Comparison of beta regression predictions against observed LSAC data
Comparison to published % Fat mass data
Below are estimated % fat mass curves using the National Health and Nutrition Examination Survey (NHANES) IV (Laurson, Eisenmann and Welk, 2011,https://www.cooperinstitute.org/vault/2440/web/files/787.pdf ). we can see that the plots have a close resemblance to the predicted beta regression estimates.
The predictions generally follow the shape of NHANES data (Figure 13 vs Figure 14), over similar age ranges.
Figure 13: % fat mass curves using National Health and Nutrition Examination Survey (NHANES)
Figure 14: Quantile estimates of beta regression predictions
Quantile output tables
Laurson, Eisenmann and Welk: NHANES LMS Estimates
Similarly, NHANES LMS Estimates percentiles shows some agreement between published and predicted % fat mass.
Figure 15: Quantile estimates of % fat mass curves using National Health and Nutrition Examination Survey (NHANES)
| gender | age | p_2 | p_5 | p_10 | p_25 | p_50 | p_75 | p_85 | p_90 | p_95 | p_98 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Male | 2 | 10.38 | 10.9 | 11.47 | 12.34 | 13.51 | 14.79 | 15.56 | 16.14 | 17.1 | 18.53 |
| Male | 3 | 10.68 | 11.4 | 11.91 | 12.75 | 13.92 | 15.43 | 16.28 | 17.09 | 18.37 | 19.93 |
| Male | 4 | 11.71 | 12.39 | 13.03 | 14.07 | 15.42 | 16.98 | 18.06 | 19 | 20.9 | 24.45 |
| Male | 5 | 11.56 | 12.38 | 13.11 | 14.35 | 15.72 | 17.48 | 18.81 | 20.05 | 22.74 | 27.19 |
| Male | 6 | 12.18 | 12.95 | 13.66 | 14.88 | 16.46 | 18.73 | 20.55 | 22.28 | 25.81 | 31.17 |
| Male | 7 | 11.91 | 12.84 | 13.49 | 14.83 | 16.53 | 18.88 | 20.75 | 23.53 | 28.45 | 35.85 |
| Male | 8 | 12.25 | 13.15 | 13.89 | 15.53 | 17.72 | 21.32 | 24.97 | 27.66 | 33.73 | 40.69 |
| Male | 9 | 11.95 | 12.99 | 13.88 | 15.54 | 18.33 | 22.69 | 26.63 | 30.03 | 37.32 | 44.18 |
| Male | 10 | 12.31 | 13.29 | 14.26 | 16.31 | 19.52 | 25.52 | 29.78 | 33.24 | 39.33 | 46.28 |
| Male | 11 | 11.91 | 13.34 | 14.04 | 16.13 | 19.74 | 27.08 | 30.91 | 33.39 | 38.01 | 45.62 |
| Male | 12 | 11.14 | 12.37 | 13.46 | 15.74 | 19.16 | 25.4 | 30.4 | 33.69 | 38.67 | 43.75 |
| Male | 13 | 10.49 | 11.7 | 13.16 | 15.32 | 18.98 | 25.3 | 30.14 | 32.99 | 36.31 | 40.27 |
| Male | 14 | 10.35 | 11.39 | 12.73 | 14.87 | 18.68 | 24.78 | 29 | 31.08 | 34.58 | 38.16 |
| Male | 15 | 9.78 | 11.22 | 12.6 | 15.07 | 18.85 | 25.17 | 28.81 | 31.5 | 34.22 | 36.24 |
| Male | 16 | 10.01 | 11.67 | 12.92 | 15.48 | 19.19 | 25.26 | 28.31 | 30.56 | 32.2 | 35.62 |
| Male | 17 | 9.98 | 11.33 | 13.02 | 16.07 | 20.86 | 26.79 | 29.8 | 31.39 | 33.3 | 35.84 |
| Female | 2 | 11.24 | 11.8 | 12.33 | 13.2 | 14.31 | 15.67 | 16.49 | 17.23 | 18.31 | 19.86 |
| Female | 3 | 11.35 | 12.03 | 12.56 | 13.5 | 14.63 | 16.2 | 17.11 | 17.8 | 18.79 | 20.77 |
| Female | 4 | 12.84 | 13.57 | 14.21 | 15.3 | 16.73 | 18.52 | 19.76 | 20.98 | 23.16 | 26 |
| Female | 5 | 12.93 | 13.78 | 14.32 | 15.61 | 17.06 | 19.06 | 20.62 | 21.59 | 23.84 | 27.49 |
| Female | 6 | 13.66 | 14.46 | 15.2 | 16.55 | 18.31 | 20.68 | 22.42 | 24.2 | 27.99 | 32.81 |
| Female | 7 | 13.8 | 14.46 | 15.26 | 16.74 | 18.64 | 21.47 | 23.82 | 25.84 | 30.17 | 35.18 |
| Female | 8 | 13.83 | 15.03 | 15.88 | 17.7 | 20.18 | 24.24 | 27.21 | 29.71 | 34.42 | 39.25 |
| Female | 9 | 13.51 | 14.7 | 15.64 | 17.57 | 20.48 | 25.12 | 28.75 | 31.28 | 35.06 | 41.75 |
| Female | 10 | 13.34 | 14.74 | 15.82 | 18.26 | 21.65 | 27.06 | 31.96 | 34.6 | 38.91 | 42.15 |
| Female | 11 | 13.55 | 14.72 | 16.2 | 18.58 | 22.39 | 28.46 | 31.95 | 34.21 | 38.43 | 41.29 |
| Female | 12 | 13.22 | 14.9 | 16.46 | 19.44 | 23.74 | 29.43 | 33.26 | 35.87 | 39.26 | 41.03 |
| Female | 13 | 13.79 | 15.38 | 16.82 | 20.18 | 24.61 | 30.74 | 34.18 | 37.05 | 39.74 | 40.64 |
| Female | 14 | 13.95 | 16.34 | 18.36 | 21.93 | 26.15 | 31.96 | 35.97 | 38.37 | 39.83 | 41.14 |
| Female | 15 | 15.71 | 16.71 | 19.41 | 23.08 | 27.73 | 33.09 | 35.99 | 37.83 | 40.02 | 41.29 |
| Female | 16 | 15.5 | 17.51 | 19.77 | 23.87 | 28.73 | 34.75 | 38.19 | 39.29 | 40.58 | 41.36 |
| Female | 17 | 16.57 | 18.34 | 19.77 | 24.07 | 29.14 | 35.21 | 38.11 | 39.69 | 40.46 | 41.56 |
Conclusion
In this report we present the analysis of the Growing up Australia : Longitudinal Study of Australian Children (LSAC) data. The report summarises the body weight, height and % fat mass used as input for the DiSCAO model. There was much consideration when estimating the % fat mass. Ultimately, beta regression was used to estimate the %fat-mass using body weight and height relationships. Where main wave LSAC estimates of %fat-mass were used as validation of beta-regression predictions. These estimates were shown to follow published estimates, suggesting good internal and external validity. All numerical estimates used as inputs and uncertainty are presented in the appendix.
Appendix : Input variables for DiSCAO
Weight
| Gender | Age Groups | BMI | Estimate | lcl | ucl |
|---|---|---|---|---|---|
| Male | Age 2 | Underweight & Healthy weight | 14.30 | 14.20 | 14.45 |
| Male | Age 2 | Overweight | 16.20 | 16.10 | 16.40 |
| Male | Age 2 | With obesity | 18.00 | 17.60 | 18.70 |
| Male | Age 3 5 | Underweight & Healthy weight | 18.10 | 18.00 | 18.20 |
| Male | Age 3 5 | Overweight | 20.70 | 20.55 | 20.90 |
| Male | Age 3 5 | With obesity | 24.50 | 24.20 | 24.90 |
| Male | Age 6 8 | Underweight & Healthy weight | 24.65 | 24.50 | 24.80 |
| Male | Age 6 8 | Overweight | 30.60 | 30.35 | 31.05 |
| Male | Age 6 8 | With obesity | 40.10 | 39.00 | 42.00 |
| Male | Age 9 11 | Underweight & Healthy weight | 33.80 | 33.70 | 34.10 |
| Male | Age 9 11 | Overweight | 45.30 | 44.90 | 46.00 |
| Male | Age 9 11 | With obesity | 60.70 | 59.20 | 62.50 |
| Male | Age 12 14 | Underweight & Healthy weight | 47.10 | 46.80 | 47.60 |
| Male | Age 12 14 | Overweight | 64.10 | 63.30 | 65.00 |
| Male | Age 12 14 | With obesity | 85.00 | 82.60 | 87.60 |
| Male | Age 15 17 | Underweight & Healthy weight | 63.90 | 63.30 | 64.40 |
| Male | Age 15 17 | Overweight | 82.20 | 80.80 | 83.90 |
| Male | Age 15 17 | With obesity | 106.50 | 101.70 | 109.80 |
| Male | Age 18 19 | Underweight & Healthy weight | 70.00 | 69.00 | 74.00 |
| Male | Age 18 19 | Overweight | 85.00 | 84.00 | 88.00 |
| Male | Age 18 19 | With obesity | 108.00 | 104.00 | 114.00 |
| Male | Age 20 24 | Underweight & Healthy weight | 70.00 | 69.00 | 74.00 |
| Male | Age 20 24 | Overweight | 85.00 | 84.00 | 88.00 |
| Male | Age 20 24 | With obesity | 108.00 | 104.00 | 114.00 |
| Male | Age 25 29 | Underweight & Healthy weight | 72.00 | 70.00 | 75.00 |
| Male | Age 25 29 | Overweight | 85.00 | 83.00 | 89.00 |
| Male | Age 25 29 | With obesity | 104.00 | 99.00 | 112.00 |
| Male | Age 30 34 | Underweight & Healthy weight | 72.00 | 71.00 | 75.00 |
| Male | Age 30 34 | Overweight | 86.00 | 84.00 | 88.00 |
| Male | Age 30 34 | With obesity | 105.00 | 104.00 | 111.00 |
| Male | Age 35 39 | Underweight & Healthy weight | 72.00 | 70.00 | 75.00 |
| Male | Age 35 39 | Overweight | 87.00 | 86.00 | 89.00 |
| Male | Age 35 39 | With obesity | 103.00 | 102.00 | 107.00 |
| Male | Age 40 44 | Underweight & Healthy weight | 72.00 | 70.00 | 75.00 |
| Male | Age 40 44 | Overweight | 86.00 | 85.00 | 88.00 |
| Male | Age 40 44 | With obesity | 103.00 | 100.00 | 107.00 |
| Male | Age 45 49 | Underweight & Healthy weight | 71.00 | 70.00 | 73.00 |
| Male | Age 45 49 | Overweight | 85.00 | 84.00 | 88.00 |
| Male | Age 45 49 | With obesity | 102.00 | 101.00 | 109.00 |
| Female | Age 2 | Underweight & Healthy weight | 13.60 | 13.55 | 13.75 |
| Female | Age 2 | Overweight | 15.65 | 15.50 | 15.90 |
| Female | Age 2 | With obesity | 17.60 | 17.30 | 18.10 |
| Female | Age 3 5 | Underweight & Healthy weight | 17.45 | 17.40 | 17.55 |
| Female | Age 3 5 | Overweight | 20.45 | 20.30 | 20.60 |
| Female | Age 3 5 | With obesity | 24.80 | 24.30 | 25.45 |
| Female | Age 6 8 | Underweight & Healthy weight | 24.20 | 24.10 | 24.40 |
| Female | Age 6 8 | Overweight | 31.10 | 30.70 | 31.45 |
| Female | Age 6 8 | With obesity | 40.20 | 39.30 | 41.60 |
| Female | Age 9 11 | Underweight & Healthy weight | 34.00 | 33.70 | 34.30 |
| Female | Age 9 11 | Overweight | 47.30 | 46.70 | 48.00 |
| Female | Age 9 11 | With obesity | 62.40 | 60.00 | 64.70 |
| Female | Age 12 14 | Underweight & Healthy weight | 48.60 | 48.20 | 49.00 |
| Female | Age 12 14 | Overweight | 64.20 | 63.60 | 65.00 |
| Female | Age 12 14 | With obesity | 86.10 | 83.00 | 87.80 |
| Female | Age 15 17 | Underweight & Healthy weight | 56.60 | 56.10 | 57.30 |
| Female | Age 15 17 | Overweight | 73.60 | 72.60 | 75.00 |
| Female | Age 15 17 | With obesity | 97.70 | 93.70 | 99.60 |
| Female | Age 18 19 | Underweight & Healthy weight | 58.00 | 57.00 | 60.00 |
| Female | Age 18 19 | Overweight | 72.00 | 70.00 | 74.00 |
| Female | Age 18 19 | With obesity | 91.00 | 89.00 | 98.00 |
| Female | Age 20 24 | Underweight & Healthy weight | 58.00 | 57.00 | 60.00 |
| Female | Age 20 24 | Overweight | 72.00 | 70.00 | 74.00 |
| Female | Age 20 24 | With obesity | 91.00 | 89.00 | 98.00 |
| Female | Age 25 29 | Underweight & Healthy weight | 59.00 | 58.00 | 62.00 |
| Female | Age 25 29 | Overweight | 74.00 | 72.00 | 76.00 |
| Female | Age 25 29 | With obesity | 91.00 | 89.00 | 97.00 |
| Female | Age 30 34 | Underweight & Healthy weight | 59.00 | 58.00 | 61.00 |
| Female | Age 30 34 | Overweight | 72.00 | 71.00 | 75.00 |
| Female | Age 30 34 | With obesity | 94.00 | 92.00 | 102.00 |
| Female | Age 35 39 | Underweight & Healthy weight | 58.00 | 57.00 | 60.00 |
| Female | Age 35 39 | Overweight | 71.00 | 70.00 | 73.00 |
| Female | Age 35 39 | With obesity | 88.00 | 85.00 | 93.00 |
| Female | Age 40 44 | Underweight & Healthy weight | 59.00 | 59.00 | 62.00 |
| Female | Age 40 44 | Overweight | 73.00 | 71.00 | 76.00 |
| Female | Age 40 44 | With obesity | 91.00 | 90.00 | 96.00 |
| Female | Age 45 49 | Underweight & Healthy weight | 59.00 | 58.00 | 61.00 |
| Female | Age 45 49 | Overweight | 72.00 | 71.00 | 74.00 |
| Female | Age 45 49 | With obesity | 88.00 | 86.00 | 91.00 |
Heights
| Gender | Age Groups | BMI | Estimate | lcl | ucl |
|---|---|---|---|---|---|
| Male | Age 2 | Underweight & Healthy weight | 94.00 | 93.90 | 94.25 |
| Male | Age 2 | Overweight | 94.45 | 94.00 | 95.00 |
| Male | Age 2 | With obesity | 95.10 | 94.00 | 96.00 |
| Male | Age 3 5 | Underweight & Healthy weight | 108.00 | 107.90 | 108.30 |
| Male | Age 3 5 | Overweight | 108.60 | 108.40 | 109.00 |
| Male | Age 3 5 | With obesity | 111.45 | 110.50 | 112.00 |
| Male | Age 6 8 | Underweight & Healthy weight | 125.35 | 125.05 | 125.65 |
| Male | Age 6 8 | Overweight | 127.70 | 127.35 | 128.10 |
| Male | Age 6 8 | With obesity | 130.55 | 129.70 | 131.70 |
| Male | Age 9 11 | Underweight & Healthy weight | 141.90 | 141.40 | 142.15 |
| Male | Age 9 11 | Overweight | 144.75 | 144.30 | 145.30 |
| Male | Age 9 11 | With obesity | 147.25 | 145.80 | 148.95 |
| Male | Age 12 14 | Underweight & Healthy weight | 159.90 | 159.40 | 160.40 |
| Male | Age 12 14 | Overweight | 162.45 | 161.90 | 163.35 |
| Male | Age 12 14 | With obesity | 164.40 | 162.80 | 166.40 |
| Male | Age 15 17 | Underweight & Healthy weight | 176.00 | 175.50 | 176.60 |
| Male | Age 15 17 | Overweight | 176.50 | 175.35 | 177.40 |
| Male | Age 15 17 | With obesity | 177.30 | 175.35 | 179.60 |
| Male | Age 18 19 | Underweight & Healthy weight | 178.00 | 177.00 | 182.00 |
| Male | Age 18 19 | Overweight | 179.00 | 178.00 | 180.00 |
| Male | Age 18 19 | With obesity | 181.00 | 179.00 | 184.00 |
| Male | Age 20 24 | Underweight & Healthy weight | 178.00 | 177.00 | 182.00 |
| Male | Age 20 24 | Overweight | 179.00 | 178.00 | 180.00 |
| Male | Age 20 24 | With obesity | 181.00 | 179.00 | 184.00 |
| Male | Age 25 29 | Underweight & Healthy weight | 178.00 | 177.00 | 180.00 |
| Male | Age 25 29 | Overweight | 178.00 | 176.00 | 180.00 |
| Male | Age 25 29 | With obesity | 178.00 | 175.00 | 182.00 |
| Male | Age 30 34 | Underweight & Healthy weight | 178.00 | 177.00 | 181.00 |
| Male | Age 30 34 | Overweight | 179.00 | 178.00 | 181.00 |
| Male | Age 30 34 | With obesity | 179.00 | 177.00 | 183.00 |
| Male | Age 35 39 | Underweight & Healthy weight | 177.00 | 176.00 | 178.00 |
| Male | Age 35 39 | Overweight | 179.00 | 178.00 | 181.00 |
| Male | Age 35 39 | With obesity | 177.00 | 175.00 | 179.00 |
| Male | Age 40 44 | Underweight & Healthy weight | 176.00 | 175.00 | 178.00 |
| Male | Age 40 44 | Overweight | 177.00 | 176.00 | 178.00 |
| Male | Age 40 44 | With obesity | 177.00 | 175.00 | 179.00 |
| Male | Age 45 49 | Underweight & Healthy weight | 175.00 | 174.00 | 177.00 |
| Male | Age 45 49 | Overweight | 176.00 | 175.00 | 178.00 |
| Male | Age 45 49 | With obesity | 176.00 | 175.00 | 178.00 |
| Female | Age 2 | Underweight & Healthy weight | 92.50 | 92.30 | 92.90 |
| Female | Age 2 | Overweight | 93.30 | 93.00 | 93.75 |
| Female | Age 2 | With obesity | 93.30 | 92.25 | 94.75 |
| Female | Age 3 5 | Underweight & Healthy weight | 106.80 | 106.55 | 107.00 |
| Female | Age 3 5 | Overweight | 108.00 | 107.60 | 108.50 |
| Female | Age 3 5 | With obesity | 110.00 | 109.05 | 111.00 |
| Female | Age 6 8 | Underweight & Healthy weight | 124.25 | 124.00 | 124.60 |
| Female | Age 6 8 | Overweight | 127.15 | 126.60 | 127.65 |
| Female | Age 6 8 | With obesity | 129.80 | 128.90 | 131.00 |
| Female | Age 9 11 | Underweight & Healthy weight | 141.50 | 141.20 | 141.95 |
| Female | Age 9 11 | Overweight | 145.75 | 145.00 | 146.55 |
| Female | Age 9 11 | With obesity | 147.30 | 146.00 | 148.95 |
| Female | Age 12 14 | Underweight & Healthy weight | 159.00 | 158.80 | 159.50 |
| Female | Age 12 14 | Overweight | 160.50 | 159.95 | 161.10 |
| Female | Age 12 14 | With obesity | 161.90 | 160.60 | 163.55 |
| Female | Age 15 17 | Underweight & Healthy weight | 164.15 | 163.70 | 164.70 |
| Female | Age 15 17 | Overweight | 164.05 | 162.80 | 164.85 |
| Female | Age 15 17 | With obesity | 164.50 | 163.50 | 167.30 |
| Female | Age 18 19 | Underweight & Healthy weight | 164.00 | 163.00 | 165.00 |
| Female | Age 18 19 | Overweight | 164.00 | 163.00 | 167.00 |
| Female | Age 18 19 | With obesity | 163.00 | 159.00 | 168.00 |
| Female | Age 20 24 | Underweight & Healthy weight | 164.00 | 163.00 | 165.00 |
| Female | Age 20 24 | Overweight | 164.00 | 163.00 | 167.00 |
| Female | Age 20 24 | With obesity | 163.00 | 159.00 | 168.00 |
| Female | Age 25 29 | Underweight & Healthy weight | 164.00 | 164.00 | 166.00 |
| Female | Age 25 29 | Overweight | 164.00 | 163.00 | 166.00 |
| Female | Age 25 29 | With obesity | 165.00 | 165.00 | 169.00 |
| Female | Age 30 34 | Underweight & Healthy weight | 165.00 | 165.00 | 167.00 |
| Female | Age 30 34 | Overweight | 164.00 | 163.00 | 166.00 |
| Female | Age 30 34 | With obesity | 165.00 | 164.00 | 169.00 |
| Female | Age 35 39 | Underweight & Healthy weight | 163.00 | 162.00 | 165.00 |
| Female | Age 35 39 | Overweight | 163.00 | 162.00 | 165.00 |
| Female | Age 35 39 | With obesity | 163.00 | 161.00 | 165.00 |
| Female | Age 40 44 | Underweight & Healthy weight | 163.00 | 161.00 | 165.00 |
| Female | Age 40 44 | Overweight | 163.00 | 162.00 | 166.00 |
| Female | Age 40 44 | With obesity | 162.00 | 161.00 | 165.00 |
| Female | Age 45 49 | Underweight & Healthy weight | 164.00 | 164.00 | 165.00 |
| Female | Age 45 49 | Overweight | 161.00 | 160.00 | 163.00 |
| Female | Age 45 49 | With obesity | 162.00 | 160.00 | 164.00 |
% Fat-mass
| Gender | Age Groups | BMI | % FM | LCL | UCL |
|---|---|---|---|---|---|
| Female | Age 2 | Underweight & Healthy weight | 13.77 | 13.68 | 13.85 |
| Female | Age 2 | Overweight | 16.42 | 16.25 | 16.59 |
| Female | Age 2 | with Obese | 19.83 | 19.03 | 20.62 |
| Male | Age 2 | Underweight & Healthy weight | 12.99 | 12.91 | 13.07 |
| Male | Age 2 | Overweight | 15.74 | 15.58 | 15.89 |
| Male | Age 2 | with Obese | 18.38 | 17.70 | 19.06 |
| Female | Age 3 5 | Underweight & Healthy weight | 15.83 | 15.76 | 15.89 |
| Female | Age 3 5 | Overweight | 19.76 | 19.64 | 19.88 |
| Female | Age 3 5 | with Obese | 26.10 | 25.55 | 26.66 |
| Male | Age 3 5 | Underweight & Healthy weight | 14.70 | 14.64 | 14.75 |
| Male | Age 3 5 | Overweight | 18.50 | 18.39 | 18.61 |
| Male | Age 3 5 | with Obese | 25.25 | 24.60 | 25.91 |
| Female | Age 6 8 | Underweight & Healthy weight | 18.16 | 18.08 | 18.25 |
| Female | Age 6 8 | Overweight | 25.60 | 25.43 | 25.77 |
| Female | Age 6 8 | with Obese | 35.47 | 34.80 | 36.14 |
| Male | Age 6 8 | Underweight & Healthy weight | 16.28 | 16.21 | 16.36 |
| Male | Age 6 8 | Overweight | 23.96 | 23.77 | 24.14 |
| Male | Age 6 8 | with Obese | 36.23 | 35.45 | 37.02 |
| Female | Age 9 11 | Underweight & Healthy weight | 19.68 | 19.52 | 19.85 |
| Female | Age 9 11 | Overweight | 30.79 | 30.50 | 31.08 |
| Female | Age 9 11 | with Obese | 40.52 | 39.72 | 41.32 |
| Male | Age 9 11 | Underweight & Healthy weight | 17.60 | 17.44 | 17.75 |
| Male | Age 9 11 | Overweight | 28.88 | 28.60 | 29.17 |
| Male | Age 9 11 | with Obese | 43.52 | 42.47 | 44.58 |
| Female | Age 12 14 | Underweight & Healthy weight | 22.25 | 22.06 | 22.45 |
| Female | Age 12 14 | Overweight | 34.50 | 34.26 | 34.75 |
| Female | Age 12 14 | with Obese | 40.12 | 39.64 | 40.61 |
| Male | Age 12 14 | Underweight & Healthy weight | 17.39 | 17.23 | 17.56 |
| Male | Age 12 14 | Overweight | 29.66 | 29.33 | 30.00 |
| Male | Age 12 14 | with Obese | 38.86 | 38.20 | 39.51 |
| Female | Age 15 17 | Underweight & Healthy weight | 25.34 | 25.01 | 25.66 |
| Female | Age 15 17 | Overweight | 36.85 | 36.59 | 37.12 |
| Female | Age 15 17 | with Obese | 39.59 | 38.95 | 40.24 |
| Male | Age 15 17 | Underweight & Healthy weight | 17.47 | 17.19 | 17.75 |
| Male | Age 15 17 | Overweight | 28.69 | 28.28 | 29.09 |
| Male | Age 15 17 | with Obese | 32.89 | 32.17 | 33.61 |
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Pop!_OS 20.04 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Sydney
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] survey_4.2-1 survival_3.5-7 Matrix_1.6-1 kableExtra_1.3.4
## [5] blandr_0.5.1 betareg_3.1-4 zoo_1.8-12 ggpubr_0.6.0
## [9] RColorBrewer_1.1-3 pillar_1.9.0 openxlsx_4.2.5.2 ggridges_0.5.4
## [13] broom_1.0.5 plotly_4.10.3 reshape2_1.4.4 lubridate_1.9.2
## [17] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.2
## [21] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3
## [25] tidyverse_2.0.0 foreign_0.8-86
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 gridExtra_2.3 inline_0.3.19
## [4] sandwich_3.0-2 rlang_1.1.1 magrittr_2.0.3
## [7] matrixStats_1.0.0 compiler_4.3.2 loo_2.6.0
## [10] mgcv_1.9-0 flexmix_2.3-19 systemfonts_1.0.4
## [13] callr_3.7.3 vctrs_0.6.3 rvest_1.0.3
## [16] pkgconfig_2.0.3 crayon_1.5.2 fastmap_1.1.1
## [19] ellipsis_0.3.2 backports_1.4.1 rmdformats_1.0.4
## [22] labeling_0.4.2 utf8_1.2.3 rmarkdown_2.24
## [25] tzdb_0.4.0 ps_1.7.5 densEstBayes_1.0-2.2
## [28] xfun_0.40 modeltools_0.2-23 cachem_1.0.8
## [31] jsonlite_1.8.7 highr_0.10 parallel_4.3.2
## [34] prettyunits_1.1.1 R6_2.5.1 bslib_0.5.1
## [37] stringi_1.7.12 StanHeaders_2.26.28 car_3.1-2
## [40] lmtest_0.9-40 jquerylib_0.1.4 Rcpp_1.0.11
## [43] bookdown_0.35.1 rstan_2.26.23 knitr_1.44
## [46] splines_4.3.2 nnet_7.3-19 timechange_0.2.0
## [49] tidyselect_1.2.0 rstudioapi_0.15.0 abind_1.4-5
## [52] yaml_2.3.7 codetools_0.2-19 processx_3.8.2
## [55] pkgbuild_1.4.2 lattice_0.22-5 plyr_1.8.8
## [58] withr_2.5.0 evaluate_0.21 RcppParallel_5.1.7
## [61] zip_2.3.0 xml2_1.3.5 carData_3.0-5
## [64] stats4_4.3.2 generics_0.1.3 hms_1.1.3
## [67] rstantools_2.3.1.1 munsell_0.5.0 scales_1.2.1
## [70] jmvcore_2.4.7 glue_1.6.2 lazyeval_0.2.2
## [73] tools_4.3.2 data.table_1.14.8 SparseM_1.81
## [76] webshot_0.5.5 ggsignif_0.6.4 mitools_2.4
## [79] crosstalk_1.2.1 QuickJSR_1.0.6 colorspace_2.1-0
## [82] nlme_3.1-163 Formula_1.2-5 cli_3.6.1
## [85] fansi_1.0.4 viridisLite_0.4.2 svglite_2.1.1
## [88] gtable_0.3.4 rstatix_0.7.2 sass_0.4.7
## [91] digest_0.6.33 htmlwidgets_1.6.2 farver_2.1.1
## [94] htmltools_0.5.6 lifecycle_1.0.3 httr_1.4.7